铁电存储器(FRAM)在汽车行驶记录仪中的优势

中国的汽车行驶记录仪中的数据应该包括两个部分,一部分为汽车实时数据(存放汽车发生事故前后的数据),另一部分为汽车历史数据(存放汽车过去一段时间内的行驶状况).汽车实时数据主要是用于分析事故发生的原因和事故的责任,汽车历史数据作为事故分析的参考依据和对汽车和司机的运行状况考核,历史数据是以当前汽车实时数据为依据.     

汽车行驶状态记录仪系统设计

汽车行驶状态记录仪是一种安装在汽车上实现类似于飞机黑匣子功能的一种设备,它可以全程记录汽车行驶数据,并通过对所记录的行驶信息数据进行分析从而对车辆的各种状况予以精确的掌控,利用它可有效预防驾驶员违章驾驶、遏制重大交通事故,具有准确性、高效性和易用性等特点.本文将介绍如何用51单片机实现汽车行驶状态记录仪系统的设计.设计...     

汽车行驶记录仪的数据传输设计

记录仪上记录的数据是管理和事故分析的关键资料,如果数据采集方式不科学,传输不方便,那么对汽车行驶记录仪产品,甚至整个汽车行驶记录仪行业而言无疑是沉重的打击.     

基于ARM的汽车“黑匣子”设计

本文设计的基于TI公司的TMS470R1A256汽车行驶记录仪可以记录汽车行驶的状态,并对通过汽车行驶状况的记录分析、鉴定道路交通事故,提高交通管理执法水平和运输管理水平,保障车辆运行安全具有重要作用。文中主要给出了系统的硬件设计及主程序的软件设计。本文在传统记录仪的基础上,增加GPRS,GPS及USB主机等功能,实现对正在行驶的汽车进行远程监控、卫星导航等功能。     

防碰撞无线通信汽车黑匣子的系统设计

采用无线模块与汽车行驶记录仪结合的方法,配合PC机可以实时且较准确地获取汽车行驶记录仪的各项信息。详细阐述添加无线模块的汽车行驶记录仪的系统设计,该系统集数据存储、数据采集、数据分析为一体,着重分析了汽车行驶记录仪的硬件结构设计和软件设计,并对实际应用中产生的无线碰撞问题提出了解决方案。     

基于WindowsCE嵌入式系统的汽车行驶记录仪的研究

汽车行驶记录仪是一种实时收集和记录汽车运行状况的电子设备,在汽车运行中汽车行驶记录仪显示出越来越重要的地位。本文通过对基于WindowsCE嵌入式系统在汽车行驶记录仪中的应用,分析和总结了微处理器、信息储存、信息传输、数据收集等汽车行驶记录仪内部等硬件设计与软件设计的主要步骤。     

汽车行驶记录仪的质量检测和管理

现代社会交通繁忙,物流发达,安装汽车行驶记录仪后,能真实、准确反映车辆运行中的实际状况,记录相关的监控数据。可以达到优化调度管理,降低运营成本,及时提醒、防止事故和用于路面的监督执法及事故分析。但是通过几年的检测和监督抽查工作,发现现在产品和管理上还存在问题。本文通过对产品质量检测和原因分析,提出了该产品的质量提升和加强管理的一些方法,以便堵塞漏洞,使该产品的质量能够比较稳定,提高可靠性和安全性。     

汽车驾驶行为远程监控分析系统的研究及应用

选用汽车无线数据记录仪,整合网络服务平台和上位机数据分析软件,构建一套简单易用的驾驶行为远程监控分析系统;利用该系统对汽车GPS定位、车速、三轴加速度等行驶数据远程采集,并结合道路状况对驾驶人在城市快速路、城市红绿灯路口、弯道、过减速带驾驶的几种典型路况驾驶行为特征数据进行对比分析;实验结果表明:该系统能有效记录、传输汽车行驶数据,通过数据的分析识别驾驶人驾驶行为特征以判断驾驶行为并评价优劣,对改进驾驶人驾驶行为、降低交通事故发生率提供有效技术支持。     

基于单片机的汽车行驶记录仪的设计

汽车行驶记录仪是对车辆行驶速度、时间、里程等状态信息进行记录、存储和输出的数字式电子记录装置.记录仪的使用,对遏止疲劳驾驶、车辆超速等交通违章行为,保障车辆行驶安全以及对道路交通事故的分析鉴定都具有重要的作用.依照国标GB/T19056-2003的要求,基于C8051F022设计了汽车行驶记录仪,给出了系统的基本组成原理、主要接口电路和软件设计方案.     

无线传输在汽车行驶记录仪中的应用

提出并实现了无线传输在汽车行驶记录中的使用方法。将无线模块添加到汽车行驶记录仪中,并设计了无线传输协议,用于快速检测记录仪中的数据,如超时、超速等。试验测试结果表明,该方法能够移动,远程、快速、准确地获取信息,具有较好的性价比以及推广价值。     

Driving behavior recognition and prediction based on Bayesian model

Since the existing intelligent driving systems are lack of efficiency and accuracy when processing huge number of driving data,a brand new approach of processing driving data was developed to identify and predicate human driving behavior based on Bayesian model.The approach was proposed to take two steps to deduce the specific driving behavior from driving data correspondingly without any supervision,the first step being using Bayesian model segmentation algorithm to divide driving data that inertial sensor collected into near-linear segments with the help of Bayesian model segmentation algorithm,and the second step being using extended LDA model to aggregate those linear segments into specific driving behavior (such as braking,turning,acceleration and coasting).Both offline and online experiments are conducted to verify this approach and it turns out that approach has higher efficiency and recognition accuracy when dealing with numerous driving data.     

A two-level stochastic approach to optimize the energy management strategy for fixed-route hybrid electric vehicles

Many hybrid electric vehicle (HEV) energy management strategies are developed and evaluated under fixed driving cycles. However in the real-world driving, vehicles are very unlikely to keep running under a fixed known cycle. Instead, a lot of vehicles run on fixed routes. Unfortunately, human driving data collected on a driving simulator shows that it is very difficult to select or create a determined typical driving cycle to represent the fixed-route driving due to the uncertainties in traffic light stops and driver behaviors. This paper presents a two-level stochastic approach to optimize the energy management strategy for fixed-route HEVs. The historical data on the fixed route are utilized and a road-segment-based stochastic HEV energy consumption model is built. The higher-level energy optimization problem is solved by stochastic dynamic programming (SDP). The SDP computation uses the vehicle model and historical driving data on the fixed route and it can be conducted offline. The result of SDP is a 2-dimension lookup table of parameters for lower-level control strategy therefore this approach can be easily real-time implemented in practice. The developed stochastic approach is compared with three strategies using the data collected on the driving simulator: the optimal energy consumption by assuming all trip information is known in advance and solved via dynamic programming (DP), a determined energy management approach using typical trip data of the fixed-route driving, and a simple strategy which does not require any route data. Simulation results show that the proposed stochastic energy management strategy consumes 1.8% more energy than the optimal result after 24 trips on the fixed route and considerably outperforms the other two real-time HEV energy management strategies.     

Driving altitude generation method with pseudo-3D building model for unmanned aerial vehicles

Spatial information is geometrical information combined with the properties of an object. In city areas where unmanned aerial vehicle (UAV) usage demand is high, it is necessary to determine the appropriate driving altitude considering the height of buildings for safe driving. In this study, we propose a data-provision method that generates the driving altitude of UAVs with a pseudo-3D building model. The pseudo-3D building model is developed using high-precision spatial information provided by the National Geographic Information Institute. The proposed method generates the driving altitude of the UAV in terms of tile information, including the UAV's starting and arrival points and a straight line between the two points, and provides the data to users. To evaluate the efficacy of the proposed method, UAV driving altitude information was generated using data of 763 551 pseudo-3D buildings in Seoul. Subsequently, the generated driving altitude data of the UAV was verified in AirSim. In addition, the execution time of the proposed method and the calculated driving altitude were analyzed.     

Categorization-based two-stage pedestrian detection system for naturalistic driving data

Understanding pedestrian behavior is a key aspect in the design and testing of a pedestrian pre-collision system. Large-scale naturalistic driving data analysis can provide valuable and objective information on how pedestrians behave in real life. Analyzing pedestrian behavior within large-scale naturalistic driving data requires an efficient pedestrian detection method. However, detecting pedestrians from large-scale naturalistic driving data is challenging due to the high pedestrian appearance variance and fast changing complicated background. In this paper, a categorization-based two-stage pedestrian detection system is proposed to efficiently locate pedestrians within our collected TASI 110-car naturalistic driving dataset. Category information including vehicle status, location, and time is automatically extracted, and efficient category-specific detection algorithms are designed for different scenario categories. The experimental results on the test set show the effectiveness of the proposed approach compared to traditional pedestrian detection methods.     

压电陶瓷驱动器非线性的电源补偿研究

针对谐振型压电陶瓷驱动器的非线性,采用电压补偿法,设计并研究了一种由单片机控制的闭环反馈、修改、生成波形数据,开不生成驱动波形的数字化电源系统。实验表明,此电源系统能很好地对原始驱动波形进行修正,使驱动器相对原始驱动波形信号是一个线性系统。     

用于多组分红外气体检测的多支路QCL驱动电源

为了使激光器电源具备驱动群量子级联激光器(QCL)的能力,研制了一种新型多支路QCL驱动电源。在硬件方面,该驱动电源采用时分复用控制技术,使各支路的驱动电流在时间上错开,达到互不影响的效果。在软件方面,对MOSFET传输特性数据进行分段拟合,提升了驱动电流线性度。实验结果表明,驱动电流线性度优于99.97%,长期稳定度优于4×10^-5。最后,利用该电源对4种不同波长的中红外QCL进行驱动试验,发光光谱正常,证明该驱动电源在实际应用中的可行性。     

疲劳驾驶警报器的设计

本设计是一个以STM32单片机为核心控制器的疲劳驾驶报警器.该疲劳驾驶报警器以压力传感器、超声波模块和光敏电阻来进行数据的收集,以蜂鸣器、语音播报模块和LED灯来反馈单片机对采集而来的数据进行处理后的结果.若驾驶员头部出现倾斜或者双手对方向盘的握力超出预设数据的范围,蜂鸣器和LED等会同时闪烁鸣叫来警醒驾驶员,若驾驶员...     

Data Supply Voltage Reduction Scheme for Low‐Power AMOLED Displays

This paper demonstrates a new driving scheme that allows reducing the supply voltage of data drivers for low‐power active matrix organic light‐emitting diode (AMOLED) displays. The proposed technique drives down the data voltage range by 50%, which subsequently diminishes in the peak power consumption of data drivers at the full white pattern by 75%. Because the gate voltage of a driving thin film transistor covers the same range as a conventional driving scheme by means of a level‐shifting scheme, the low‐data supply scheme achieves the equivalent dynamic range of OLED currents. The average power consumption of data drivers is reduced by 60% over 24 test images, and power consumption is kept below 25%.     

LED大屏幕显示补偿技术

提出了一种能够改善LED显示质量的方法。首先通过对色彩的计算分析,可以实现调节色彩纯度较高的每组LED中的三色LED的相对亮度,显示出纯度较低的系统R、G、B三基色;同时,也可以调节每组三基色LED中每个LED的亮度以达到调节此组LED的整体亮度。其次通过对LED的驱动的分析,以恒流脉冲的时间长度来控制显示不同的灰度级别,通过对每组RGBLED中每颗LED脉冲时间的增减可以调节三者的相对亮度达到所需要显示的由系统R、G、B三基色混成的色彩和整体亮度。最后则说明了实施的方法,通过在恒流下对所有LED的色彩和亮度的检测,计算出矫正色彩和亮度所需要的数据并储存起来,在驱动电路中通过自动查询的方式使得矫正后的色彩和亮度得以显示。同样,LED在不同的温度和驱动电流下所显示的色彩和亮度也有偏差,通过此方法,对驱动脉冲的调节实现显示效果的提升。     

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the driving situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the driving condition using a limited duration of driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.